This paper describes a framework for an agent to learn models of verb-phrase meanings from human teachers and combine these models with environmental dynamics to enact verb commands. The framework extends prior work in apprenticeship learning and leverages recent advancements in modeling activities and planning in domains with multiple objects. We show how to both learn a verb model as a relational finite state machine and how to turn this model into reward and heuristic functions that can then be composed with an MDP model of an environment. The resulting "combined model" can then be efficiently searched by a planner to enact a verb command in this environment. Our experiments in simulated robot domains show this framework can be used to quickly teach verb commands and improves over the current state of the art method.